Fast ground irradiance computations for agrivoltaics via physics-informed deep learning models.

ABSTRACT: Developing photovoltaic tracker algorithms for bifacial solar modules in agrivoltaic systems requires computationally intensive raytracing simulations to accurately quantify irradiation. Sunlight distribution on ground and module levels is essential for optimizing the setup and operation of tiltable PV systems, maximizing crop and electrical yield under various weather conditions and tilt configurations. We introduce a deep learning-based surrogate model that computes ground-level irradiation in a complex agrivoltaic scene with PV tracking. The surrogate model is physics-informed since the training data includes raytracing outputs based on real weather data. It computes the ground irradiance map based on direct normal irradiance, diffuse horizontal irradiance, solar position, and system ge

SUBMITTER: Kurumundayil L 

PROVIDER: S-EPMC12504610 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature